# imports
import gradio as gr
import json
import librosa
import os
import soundfile as sf
import tempfile
import uuid
import torch
from transformers import AutoTokenizer, VitsModel, set_seed, AutoModelForCausalLM, AutoTokenizer, pipeline

from nemo.collections.asr.models import ASRModel
from nemo.collections.asr.parts.utils.streaming_utils import FrameBatchMultiTaskAED
from nemo.collections.asr.parts.utils.transcribe_utils import get_buffered_pred_feat_multitaskAED



torch.random.manual_seed(0)
proc_model_name = "microsoft/Phi-3-mini-4k-instruct-gguf"
proc_model = AutoModelForCausalLM.from_pretrained(proc_model_name)
proc_model.to("cpu")
proc_tokenizer = AutoTokenizer.from_pretrained(proc_model_name)





SAMPLE_RATE = 16000 # Hz
MAX_AUDIO_MINUTES = 10 # wont try to transcribe if longer than this

model = ASRModel.from_pretrained("nvidia/canary-1b")
model.eval()

# make sure beam size always 1 for consistency
model.change_decoding_strategy(None)
decoding_cfg = model.cfg.decoding
decoding_cfg.beam.beam_size = 1
model.change_decoding_strategy(decoding_cfg)



amp_dtype = torch.float16


def convert_audio(audio_filepath, tmpdir, utt_id):

	data, sr = librosa.load(audio_filepath, sr=None, mono=True)

	duration = librosa.get_duration(y=data, sr=sr)

	if sr != SAMPLE_RATE:
		data = librosa.resample(data, orig_sr=sr, target_sr=SAMPLE_RATE)

	out_filename = os.path.join(tmpdir, utt_id + '.wav')

	# save output audio
	sf.write(out_filename, data, SAMPLE_RATE)

	return out_filename, duration

def transcribe(audio_filepath):

	if audio_filepath is None:
		raise gr.Error("Please provide some input audio: either upload an audio file or use the microphone")
	
	utt_id = uuid.uuid4()
	with tempfile.TemporaryDirectory() as tmpdir:
		converted_audio_filepath, duration = convert_audio(audio_filepath, tmpdir, str(utt_id))
        
		# make manifest file and save
		manifest_data = {
			"audio_filepath": converted_audio_filepath,
			"source_lang": "en",
			"target_lang": "en",
			"taskname": "asr",
			"pnc": "no",
			"answer": "predict",
			"duration": str(duration),
		}

		manifest_filepath = os.path.join(tmpdir, f'{utt_id}.json')

		with open(manifest_filepath, 'w') as fout:
			line = json.dumps(manifest_data)
			fout.write(line + '\n')

		output_text = model.transcribe(manifest_filepath)[0]
		
	return output_text



start = {"role": "system", "content": "You are a helpful digital assistant. Please provide safe, ethical and accurate information to the user."}



def generate_response(user_input):  
    messages = [start, {"role": "user", "content": user_input}]
    inputs = proc_tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
    with torch.no_grad():
        outputs = proc_model.generate(
            inputs, 
            max_new_tokens=48,
        )
    
    response = proc_tokenizer.batch_decode(
        outputs, 
        skip_special_tokens=True, 
        clean_up_tokenization_spaces=False,
    )[0]
    
    return response
    
def CanaryPhi(audio_filepath):
    user_input = transcribe(audio_filepath)
    response = generate_response(user_input)
    return response


# Create a Gradio interface
iface = gr.Interface(
    fn=CanaryPhi, 
    inputs=gr.Audio(sources="microphone", type="filepath"), 
    outputs=gr.Textbox(),
)

# Launch the interface
iface.launch()